We’ll start by making some histograms.
#install.packages("dslabs")
library(dslabs)
data(heights)
glimpse(heights)
## Rows: 1,050
## Columns: 2
## $ sex <fct> Male, Male, Male, Male, Male, Female, Female, Female, Female, M…
## $ height <dbl> 75, 70, 68, 74, 61, 65, 66, 62, 66, 67, 72, 72, 69, 68, 69, 66,…
This data is the heights of humans, divided by their biological sex.
Use ggplot to make a histogram of all of the heights:
ggplot(heights, aes(x = height)) + geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Change up the binwidth and see how the plots change. Try 1, 5, 10, and 20
ggplot(heights, aes(x = height)) + geom_histogram(binwidth = 1)
ggplot(heights, aes(x = height)) + geom_histogram(binwidth = 5)
ggplot(heights, aes(x = height)) + geom_histogram(binwidth = 10)
ggplot(heights, aes(x = height)) + geom_histogram(binwidth = 20)
Smooth this out to an emperical density with
geom_density()
ggplot(heights, aes(x = height)) + geom_density()
Use a new argument in the aes(), group = to
split this density by sex
ggplot(heights, aes(x = height, group = sex)) + geom_density()
OR we can do it with color or fill. If you
say you want to color by sex, R knows that you want a different curve
for each of them.
ggplot(heights, aes(x = height, fill = sex)) + geom_density()
If you’ve used fill, then there is now a slight issue that they are overlapped. We can fix this with alpha transparency!
ggplot(heights, aes(x = height, fill = sex)) + geom_density(alpha = 0.5)
Let’s make some boxplots of the same information.
ggplot(heights) + geom_boxplot(aes(x = height, y = sex))
Find the mean and median overall.
heights %>% summarise(mean_height = mean(height), median_height = median(height))
## mean_height median_height
## 1 68.32301 68.5
Find the mean and median for both groups.
heights %>% group_by(sex) %>% summarise(mean_height = mean(height), median_height = median(height))
## # A tibble: 2 × 3
## sex mean_height median_height
## <fct> <dbl> <dbl>
## 1 Female 64.9 65.0
## 2 Male 69.3 69
How tall is the tallest woman? How short is the shortest man?
heights %>% group_by(sex) %>% summarise(max(height), min(height))
## # A tibble: 2 × 3
## sex `max(height)` `min(height)`
## <fct> <dbl> <dbl>
## 1 Female 79 51
## 2 Male 82.7 50
# install.packages("pscl")
library(pscl) # loads in the package that has this data.
## Classes and Methods for R originally developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University (2002-2015),
## by and under the direction of Simon Jackman.
## hurdle and zeroinfl functions by Achim Zeileis.
## You might need to install this...
# data for presidental elections
votedata <- presidentialElections
glimpse(votedata)
## Rows: 1,097
## Columns: 4
## $ state <chr> "Alabama", "Arizona", "Arkansas", "California", "Colorado", "C…
## $ demVote <dbl> 84.76, 67.03, 86.27, 58.41, 54.81, 47.40, 48.11, 74.49, 91.60,…
## $ year <int> 1932, 1932, 1932, 1932, 1932, 1932, 1932, 1932, 1932, 1932, 19…
## $ south <lgl> TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FAL…
Let’s look at the democratic vote by state for 2000. We can’t use
geom_bar for a bar chart, since we have the category in one
variable and the “height” of the bar in another. We need
geom_col()
Make a bar graph of the democratic vote by state in 2000.
ggplot(votedata %>% filter(year == 2000)) + geom_col(aes(x = state, y = demVote))
Well this looks awful. We have two options: swap the x and y or the more fun sounding… Coordinate flip!
Use coord_flip() on the previous graph to make it
better.
ggplot(votedata %>% filter(year == 2000)) + geom_col(aes(x = state, y = demVote)) + coord_flip()
I don’t love the squashed together coordinates, but it’s a display window issue.
So. This is a helpful graph, but it would be more helpful if
it was ordered. Use x = reorder(x_variable, y_variable) in
aes() to order the x variable by the y variable
ggplot(votedata %>% filter(year == 2000)) + geom_col(aes(x = reorder(state, demVote), y = demVote)) + coord_flip()
So, what if I want to see what the north and south states did different?
start with a facet_wrap using the south variable:
ggplot(votedata %>% filter(year == 2000)) + geom_col(aes(x = reorder(state, demVote), y = demVote)) + coord_flip() + facet_wrap(~ south)
Okay, that’s not great. Lets color by south instead.
ggplot(votedata %>% filter(year == 2000)) + geom_col(aes(x = reorder(state, demVote), y = demVote, fill = south)) + coord_flip()
I’m a good data scientist, so I want my plot to have a name! and my
axes to have lables! Use labs to add a title, subtitle, and
x and y labels.
ggplot(votedata %>% filter(year == 2000)) +
geom_col(aes(x = reorder(state, demVote), y = demVote, fill = south)) +
coord_flip() +
labs(title = "Percent Dem Vote by State, in 2000",
subtitle = "Colored by State location",
x = "State",
y = "% Votes Democratic")
You can move the legend with
theme(legend.position = "bottom")
ggplot(votedata %>% filter(year == 2000)) +
geom_col(aes(x = reorder(state, demVote), y = demVote, fill = south)) +
coord_flip() +
labs(title = "Percent Dem Vote by State, in 2000",
subtitle = "Colored by State location",
x = "State",
y = "% Votes Democratic") +
theme(legend.position = "bottom")
What else could we facet by? years! Let’s filter to year in 2008 and 2016, then facet by years.
ggplot(votedata %>% filter(year == 2008 | year == 2016)) +
geom_col(aes(x = reorder(state, demVote), y = demVote, fill = south)) +
coord_flip() +
facet_wrap(~ year) +
labs(title = "Percent Dem Vote by State, in 2000",
subtitle = "Colored by State location",
x = "State",
y = "% Votes Democratic") +
theme(legend.position = "bottom")
## this has the issue of having weird sorting things, since each graph is a different ordering!
We need to know who won! We could add a vertical line at 50 for who
got more, to indicate the majority of votes. Adding the layer
geom_hline() adds a horizontal line. (What do you guess
geom_vline() would do?)
ggplot(votedata %>% filter(year == 2008 | year == 2016)) +
geom_col(aes(x = reorder(state, demVote), y = demVote, fill = south)) +
coord_flip() +
facet_wrap(~ year) +
labs(title = "Percent Dem Vote by State, in 2000",
subtitle = "Colored by State location",
x = "State",
y = "% Votes Democratic") +
theme(legend.position = "bottom") +
geom_hline(yintercept = 50)
When using geom_polygon or geom_map, you will typically need two data frames:
An id variable links the two together.
Run the below code to get a map graph.
library(maps)
##
## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
##
## map
votedata$state <- tolower(votedata$state) ## states need to be lowercase for linking
states_map <- map_data("state") ## this gives us the lat and long for each point of each state.
map_plot <- ggplot(data = votedata %>% filter(year == 2008), aes(map_id = state)) +
geom_map(aes(fill = demVote), map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat)
map_plot
map_plot <- ggplot(data = votedata %>% filter(year == 2016), aes(map_id = state)) +
geom_map(aes(fill = demVote), map = states_map)+
expand_limits(x = states_map$long, y = states_map$lat)
map_plot
What if I want a map that shows which of the states are “south”? What do I change?
map_plot <- ggplot(data = votedata %>% filter(year == 2016), aes(map_id = state)) +
geom_map(aes(fill = south), map = states_map)+
expand_limits(x = states_map$long, y = states_map$lat)
map_plot
I want to know the average democratic vote for N vs S, by year.
First, find the average democratic votes for the north and the south,
every year. You’ll need to do a double group_by()
here. You do it in one call of the function.
votedata %>% group_by(south, year) %>% summarise(mean_dem = mean(demVote))
## `summarise()` has grouped output by 'south'. You can override using the
## `.groups` argument.
## # A tibble: 44 × 3
## # Groups: south [2]
## south year mean_dem
## <lgl> <int> <dbl>
## 1 FALSE 1932 56.7
## 2 FALSE 1936 59.2
## 3 FALSE 1940 52.8
## 4 FALSE 1944 51.1
## 5 FALSE 1948 50.2
## 6 FALSE 1952 40.9
## 7 FALSE 1956 40.1
## 8 FALSE 1960 48.0
## 9 FALSE 1964 63.0
## 10 FALSE 1968 44.7
## # ℹ 34 more rows
Then, let’s plot that! Pipe the result of your group_by and summarize
to ggplot and geom_line(), with year on the x axis and your summarized
value on the y axis. Color by the south variable.
votedata %>%
group_by(south, year) %>%
summarise(mean_dem = mean(demVote)) %>%
ggplot(aes(x = year, y = mean_dem, color = south)) + geom_line()
## `summarise()` has grouped output by 'south'. You can override using the
## `.groups` argument.
Penguins!
library(palmerpenguins)
glimpse(penguins)
## Rows: 344
## Columns: 8
## $ species <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex <fct> male, female, female, NA, female, male, female, male…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
We can use boxplots to visualize the distribution of weight (body_mass_g) within each species:
ggplot(penguins, aes(x = body_mass_g, y = species)) + geom_boxplot()
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
What if we also want the points? Layering!! Add a geom_point to your existing boxplot. geom_boxplot + geom_point!
ggplot(penguins, aes(x = body_mass_g, y = species)) + geom_boxplot() + geom_point()
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
But, these are all stacked up… to actually see them, use “geom_jitter” instead of points
ggplot(penguins, aes(x = body_mass_g, y = species)) + geom_boxplot() + geom_jitter()
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
How to get the boxplots on top? The layers are plotted in the order you give them, so change to geom_point + geom_boxplot. (You might want to change the alpha on the boxplot to be able to see the plots under them)
ggplot(penguins, aes(x = body_mass_g, y = species)) + geom_jitter() + geom_boxplot(alpha = 0.5)
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
Maybe let’s try replacing the boxplot with a
geom_violin()?
ggplot(penguins, aes(x = body_mass_g, y = species)) + geom_jitter() + geom_violin(alpha = 0.5)
## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).